Elicitation and representation of expert knowledge for computer aided diagnosis in mammography

Methods of Information in Medicine

Volumn 43, Issue 3, 2004, Pages 239-246

  Alberdi, Eugenio ^a,b   Taylor, P ^a   Lee, R ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b CITY UNIVERSITY   (United Kingdom)

Author keywords

Computer aided diagnosis; Image processing; Knowledge elicitation; Knowledge representation; Radiology

Indexed keywords

ALGORITHM; ARTICLE; BENIGN TUMOR; BREAST CALCIFICATION; COMPUTER ASSISTED DIAGNOSIS; DIFFERENTIAL DIAGNOSIS; FOLLOW UP; HUMAN; IMAGE PROCESSING; MAJOR CLINICAL STUDY; MALIGNANT NEOPLASTIC DISEASE; MAMMOGRAPHY; MEASUREMENT; MEDICAL DECISION MAKING; NORMAL HUMAN; PRIORITY JOURNAL; RADIOLOGIST;

EID: 3142666102     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.1055/s-0038-1633864     Document Type: Article

References (34)

1. Taylor P. Computer aids for decision making in diagnostic radiology - a literature review. Brit J Radiol 1995; 68: 945-57.
2. Roehrig J, Castellino RA. The promise of computer aided detection in digital mammography. Eur J Radiol 1999; 31 (1): 35-9.
3. Freer TW, Ulissey MJ. Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center. Radiology 2001; 220: 781-6.
4. Schmidt F, Sorantin E, Szepesvàri C, Graif E, Becker M, Mayer H, Hartwagner K. An automatic method for the identification and interpretation of clustered micro-calcifications in mammograms. Phys Med Biol 1999; 44 (5): 1231-43.
5. Hara T, Yamada A, Fujita H et al. Automated classification method of mammographic calcifications by using artificial neural network and ACR BIRADS criteria for microcalcification distribution. In: Yaffe M (ed.). Proceedings of the 5th International Workshop on Digital Mammography. Medical Physics Publishing, 2000: pp. 198-204.
6. Kaufmann GH, Salifty MF, Granitto P, Cecccatto HA. Automated detection and classification of clustered calcifications using morphological filtering and statistical techniques. In: Yaffe M (ed.). Proceedings of the 5th International Workshop on Digital Mammography; Medical Physics Publishing, 2000: pp. 253-8.
7. Markopoulos C, Kouskos E, Koufopoulos K et al. Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography. Eur J Radiol 2001; 39: 60-5
8. Hartswood M, Procter R, Willimas J. Prompting in practice: How can we ensure radiologists make best use of computer-aided detection systems in screening mammography? In: Karssemeijer E, Thijsen M, Hendriks J (eds.). Digital Mammography. Kluwer Publishing, Dordrecht, 1998: pp. 363-70.
9. Taylor P, Alberdi E, Lee R et al. Incorporating Image Processing in a Clinical Decision Support System. In: Insana MF, Leahy RM (eds.). Information Processing in Medical Imaging. Springer Verlag, 2001: pp. 134-40.
10. Taylor P, Fox J, Todd-Pokropek A. The development and evaluation of CADMIUM: a prototype system to assist in the interpretation of mammograms. Medical Image Analysis 1999; 3: 321-37.
11. Cook HM, Fox MC. Application of expert systems to mammographic image analysis. Am J Physiol Imag 1989; 4: 16-22.
12. Stewart BD, Cairns AY, Ricketts IW. The analysis of breast abnormalities by computer. In: Gale A, Astley S, Dance D, Cairns A (eds.). Digital Mammography. Elsevier Science, Amsterdam, 1994: pp. 251-61.
13. Floyd CE, Lo JY, Tourassi GD. Case-based reasoning algorithm that uses mammographic findings for breast biopsy decisions. AJR 2000; 175: 1347-52.
14. Fox J, Myers CD, Greaves MF, Pegram S. Knowledge acquisition for expert systems: Experience in leukaemia diagnosis. Methods Inf Med 1985; 24: 65-72.
15. Cooke NJ. Varieties of knowledge elicitation techniques. International Journal of Human Computer Studies 1994; 41: 801-49.
16. Khan AS, Hoffmann A. Building a case based diet recommendation system without a knowledge engineer. Artificial Intelligence in Medicine 2003; 27 (2): 155-79.
17. Guimaraes G, Peter JH, Penzel T, Ultsch A. A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders. Artificial Intelligence in Medicine 2001; 23(3):211-37.
18. Ceusters W, Rogers J, Consorti F, Rossi-Mori A. Syntactic-semantic tagging as a mediator between linguistic representations and formal models: an exercise in linking SNOMED to GALEN. Intelligence in Medicine 1999; 15 (1): 5-23.
19. Chute CG. Clinical classification and terminology: Some history and current observations. JAMIA 2000; 7 (3): 298-303.
20. American College of Radiology. Breast imaging reporting and data system (BI-RADS). 2nd ed. Reston, VA: ACR; 1995.
21. Baker JA, Kornguth PJ, Floyd CE. Breast imaging reporting and data system standardized mammography lexicon: observer variability in lesion description. AJR 1996; 166: 773-8.
22. Liberman L, Abramson AF, Lassman JR, Morris EA, Dershaw DD. The breast imaging reporting and data system: Positive predictive value of mammographic features and final assessment categories. AJR 1997; 171: 35-40.
23. Kerlikowske K, Grady D, Barclay J, Frankel SD, Ominsky SH, Sickles EA, Ernster V. Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting Data System. Journal of the National Cancer Institute 1998; 90 (23): 1801-9.
24. Starren J, Johnson SM. Expressiveness of the Breast Imaging Reporting and Database System (BI-RADS). JAMIA 1997; Suppl. S: 655-9.
25. Simpson W, Neilson F, Kelly PJ. Descriptive Terms for mammographic abnormalities: Observer variation in application. Clin Radiol 1996; 51: 709-71.
26. Whatmough P, Gale A, Wilson ARM. Do radiologists agree on the importance of mammographic features? In: Doi K, Giger ML, Nishikawa RM, Schmidt RA (eds.): Digital Mammography'96. Elsevier Science, Amsterdam, 1996; pp. 111-6.
27. D'Orsi CJ, Getty DJ, Swets JA, et al. Reading and decision aids for improved accuracy and standardization of mammographic diagnosis. Radiology 1992; 184: 619-22.
28. Alberdi E, Taylor P, Lee R et al. CADMIUM II: Acquisition and representation of radiological knowledge for computerized decision support in mammography. In: Overhage JM (ed.). Proceedings of the American Medical Informatics Association Symposium 2000; pp. 7-11.
29. Alberdi E, Taylor P, Lee R et al. Eliciting a terminology for mammographic calcifications. Clinical Radiology 2002; 57: 1007-13.
30. Skinner MA, Swain M, Simmons R, McCarty KS, Sullivan DC, Iglehart JD. Nonpalpable breast lesions at biopsy. Ann Surg 1988; 208 (2): 203-8.
31. Franceschi D, Crowe J, Zollinger R, Duchesneau R, Shenk R, Stefanek G. Biopsy of the breast for mamographically detected lesions. Surgery, Gynecology and Obstetrics 1990; 171 (6): 449-55.
32. Monostori Z, Herman PG, Carmody DP, Eacobacci TM, Capece NR, Cruz VM, Fentin S, Vernace FM. Limitations in distinguishing malignant from benign lesions of the breast by systematic review of mamograms. Surgery, Gynecology and Obstetrics 1991; 173: 438-42.
33. Harkins K, Tartter PI, Hermann G, Squitieri R, Bower ST. Keller RJ. Multivariate analysis of roentgenologic characteristics and risks factors for nonpalpable carcinoma of the breast. Journal of the American College of Surgeons 1994; 178: 149-54.
34. Lee R, Alberdi E, Taylor P et al. A comparative study of four techniques for calcification detection. In: Yaffe M (ed.), Proceedings of the 5th International Workshop on Digital Mammography. Medical Physics Publishing 2000: pp. 264-71.